2019-09-15T11:58:58Zhttps://digital.csic.es/dspace-oai/requestoai:digital.csic.es:10261/380902016-02-17T05:06:25Zcom_10261_65com_10261_8col_10261_318DIGITAL.CSICauthorVicente Serrano, Sergio M.2011-07-28T11:49:23Z2004-10International Journal of Remote Sensing 25(20): 4325-4350 (2004)0143-1161http://hdl.handle.net/10261/3809010.1080/01431160410001712990This paper analyses and maps the spatial distribution of soil moisture using remote sensing: National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) and Landsat-Enhanced Thematic Mapper (ETM+) images. The study was carried out in the central Ebro river valley (northeast Spain), and examines the spatial relationships between the distribution of soil moisture and several meteorological and geographical variables following a long, intense dry period (winter 2000). Soil moisture estimates were obtained using thermal, visible and near-infrared data and by applying the 'triangle method', which describes relationships between surface temperature (Ts) and fractional vegetation cover (Fr). Low differences were found between the soil moisture estimates obtained using AVHRR and ETM+ sensors. Soil moisture estimated using remote sensing is close to estimations obtained from climate indices. This fact, and the high similarity between estimations of both sensors, suggests the reasonable reliability of soil moisture remote sensing estimations. Moreover, in estimations from both sensors the spatial distribution of soil moisture was largely accounted for by meteorological variables, mainly precipitation in the dry period. The results indicate the high reliability of remote sensing for determining areas affected by water deficits and for quantifying drought intensity.engopenAccessMapping soil moisture in the central Ebro river valley (northeast Spain) with Landsat and NOAA satellite imagery: a comparison with meteorological dataArtículoTGljZW5jaWEgQ1NJQyAKClBhcmEgcXVlIGVsIHJlcG9zaXRvcmlvIERpZ2l0YWwuQ1NJQyBwdWVkYSBhbG1hY2VuYXIgeSBkaXN0cmlidWlyIGVsIG9iamV0byBkaWdpdGFsIGRlcG9zaXRhZG8sIAplcyBuZWNlc2FyaW8gcXVlIGxhIHBlcnNvbmEgcXVlIGhhZ2EgZWwgZGVww7NzaXRvIGxlYSB5IGFjZXB0ZSBsYXMgY29uZGljaW9uZXMgZXN0YWJsZWNpZGFzIGVuIAplc3RhIGxpY2VuY2lhOiAKCkVsL2xvcyBhdXRvci9lcyBvIHBvc2VlZG9yL2VzIGRlbCBjb3B5cmlnaHQgZGVsIHRyYWJham8gZGVwb3NpdGFkbyBvIGVuIHN1IGNhc28gbGEgcGVyc29uYSAKZGVsZWdhZGEgcGFyYSBoYWNlcmxvLCBnYXJhbnRpemEgYWwgQ1NJQyBlbCBkZXJlY2hvIG5vIGV4Y2x1c2l2byBwYXJhIGRpc3RyaWJ1aXIsIGFsbWFjZW5hciB5IApwcmVzZXJ2YXIgZW4gZm9ybWF0byBlbGVjdHLDs25pY28gZWwgb2JqZXRvIGRpZ2l0YWwgZGVwb3NpdGFkby4KCkVsIGRlcG9zaXRhbnRlLCBlbiBjYXNvIGRlIHVuYSBvYnJhIGNvbiBtw6FzIGRlIHVuIGF1dG9yLCBnYXJhbnRpemEgcXVlIGxvIGhhY2UgcmVzcG9uc2FibGVtZW50ZSAKZW4gbm9tYnJlIHkgY29uIGNvbnNlbnRpbWllbnRvIGRlIGxvcyBkZW3DoXMgY29hdXRvcmVzLgoKRGVjbGFyYSBxdWUgc2UgdHJhdGEgZGUgdW4gdHJhYmFqbyBvcmlnaW5hbCB5IG5vIGVzdGEgc3VqZXRvIGEgcmVzdHJpY2Npb25lcyBkZSBjb3B5cmlnaHQgY29uIAp0ZXJjZXJvcyBwYXJhIHBvZGVyIG90b3JnYXIgYWwgQ1NJQyBsb3MgZGVyZWNob3MgcmVxdWVyaWRvcyBlbiBlc3RhIGxpY2VuY2lhLgoKU2kgZWwgdHJhYmFqbyBkZXBvc2l0YWRvIGNvbnRpZW5lIG1hdGVyaWFsIGRlbCBxdWUgZWwgYXV0b3Igbm8gcG9zZWUgZWwgY29weXJpZ2h0LCBlbCBhdXRvciAKZGVjbGFyYSBxdWUgaGEgb2J0ZW5pZG8gZWwgcGVybWlzbyBuZWNlc2FyaW8gZGVsIHByb3BpZXRhcmlvIGRlbCBjb3B5cmlnaHQgcGFyYSBnYXJhbnRpemFyIGFsIApDU0lDIGxvcyBkZXJlY2hvcyBkZXNjcml0b3MgZW4gZXN0YSBsaWNlbmNpYSwgeSBxdWUgZWwgcG9zZWVkb3IgZGVsIGNvcHlyaWdodCBlc3TDoSBjbGFyYW1lbnRlIAppZGVudGlmaWNhZG8geSByZWNvbm9jaWRvIGVuIGVsIHRleHRvIG8gY29udGVuaWRvIGRlbCBhcmNoaXZvIGRlcG9zaXRhZG8uCgpFbCBhdXRvciBhY2VwdGEgcXVlIGVsIENTSUMgcHVlZGUsIHNpbiByZWFsaXphciBjYW1iaW9zIGVuIGVsIGNvbnRlbmlkbywgY29udmVydGlyIGVsIHRyYWJham8gYSAKY3VhbHF1aWVyIG1lZGlvIG8gZm9ybWF0byBjb24gb2JqZXRpdm9zIGRlIHByZXNlcnZhY2nDs24uCgpBc2ltaXNtbyBlbCBhdXRvciBhY2VwdGEgcXVlIGVsIENTSUMgcHVlZGUgY29uc2VydmFyIG3DoXMgZGUgdW5hIGNvcGlhIGRlIGVzdGUgdHJhYmFqbyBwYXJhIGdhcmFudGl6YXIgCmxhIHNlZ3VyaWRhZCB5IGxhIHByZXNlcnZhY2nDs24gZGUgbG9zIGFyY2hpdm9zLgoKRWwgQ1NJQyBwcmVzZXJ2YXLDoSB5IGRpZnVuZGlyw6EgZXN0ZSB0cmFiYWpvLiBFbiBlbCBjYXNvIGRlIHF1ZSBubyBwdWVkYSBjb250aW51YXIgbWFudGVuaWVuZG8gZWwgCmFyY2hpdm8gY29tbyBwYXJ0ZSBkZWwgcmVwb3NpdG9yaW8gaW5zdGl0dWNpb25hbCBzZSByZXNlcnZhIGVsIGRlcmVjaG8gZGUgZGV2b2x2ZXIgZWwgY29udGVuaWRvIGFsIApkZXBvc2l0YW50ZS4gU2kgZXN0byBubyBlcyBwb3NpYmxlIChwb3JxdWUgbGEgY29tdW5pZGFkLCBjb2xlY2Npw7NuIGV0Yy4geWEgbm8gZXhpc3RhIG8gZWwgYXV0b3Igbm8gCmVzdMOpIGxvY2FsaXphYmxlKSwgZWwgbWF0ZXJpYWwgcG9kcsOtYSBzZXIgYXJjaGl2YWRvIGNvbW8gcGFydGUgZGVsIGFyY2hpdm8gZGlnaXRhbCBkZSBsYSBpbnN0aXR1Y2nDs24uIAoKU2kgbGEgY29udHJpYnVjacOzbiBzZSBiYXNhIGVuIHRyYWJham9zIGZpbmFuY2lhZG9zIG8gcGF0cm9jaW5hZG9zIHBvciBvcmdhbml6YWNpb25lcyBkaXN0aW50YXMgYWwgCkNTSUMsIGRlY2xhcmEgaGFiZXIgY3VtcGxpZG8gY29uIGN1YWxxdWllciBkZXJlY2hvIHkgb2JsaWdhY2nDs24gZXhwcmVzYWRvcyBlbiBlbCBjb250cmF0byBvIGFjdWVyZG8gCmNvbiBkaWNoYXMgb3JnYW5pemFjaW9uZXMuIAoKRWwgbm9tYnJlIGRlbCBkZXBvc2l0YW50ZSBxdWVkYXLDoSBjbGFyYW1lbnRlIGlkZW50aWZpY2FkbyBwb3IgZWwgQ1NJQyBjb21vIGVsIGRlbCBhdXRvciBvIHByb3BpZXRhcmlvIApkZSBsYSBjb250cmlidWNpw7NuLCB5IGVsIENTSUMgbm8gcmVhbGl6YXLDoSBuaW5ndW5hIGFsdGVyYWNpw7NuIGRlIHN1IGNvbnRyaWJ1Y2nDs24sIGV4Y2VwdG8gbGFzIHJlZmVyaWRhcyAKYWwgZm9ybWF0bywgcGVybWl0aWRhcyBwb3IgZXN0YSBsaWNlbmNpYS4KURLhttps://digital.csic.es/bitstream/10261/38090/1/international+journal+of+remote+sensing%2C+%282004%29+25%2C+4325-4350.docFileMD5213c911a262a0cdc7b1ea41d2973a1c91379840application/mswordinternational journal of remote sensing, (2004) 25, 4325-4350.docURLhttps://digital.csic.es/bitstream/10261/38090/3/international+journal+of+remote+sensing%2C+%282004%29+25%2C+4325-4350.doc.txtFileMD5cabfc9973705e89c9a47371aa3d82f0163010text/plaininternational journal of remote sensing, (2004) 25, 4325-4350.doc.txt